Biometric identification systems, i.e. the systems that are able to recognize humans by analyzing their physiological or behavioral characteristics, have gained a lot of interest in recent years. They can be used to raise the security level in certain institutions or can be treated as a convenient replacement for PINs and passwords for regular users. Automatic face recognition is one of the most popular biometric technologies, widely used even by many low-end consumer devices such as netbooks. However, even the most accurate face identification algorithm would be useless if it could be cheated by presenting a photograph of a person instead of the real face. Therefore, the proper liveness measurement is extremely important. In this paper we present a method that differentiates between video sequences showing real persons and their photographs. First we calculate the optical flow of the face region using the Farnebäck algorithm. Then we convert the motion information into images and perform the initial data selection. Finally, we apply the Support Vector Machine to distinguish between real faces and photographs. The experimental results confirm that the proposed approach could be successfully applied in practice.

In this paper a review on biometric person identification has been discussed using features from retinal fundus image. Retina recognition is claimed to be the best person identification method among the biometric recognition systems as the retina is practically impossible to forge. It is found to be most stable, reliable and most secure among all other biometric systems. Retina inherits the property of uniqueness and stability. The features used in the recognition process are either blood vessel features or non-blood vessel features. But the vascular pattern is the most prominent feature utilized by most of the researchers for retina based person identification. Processes involved in this authentication system include pre-processing, feature extraction and feature matching. Bifurcation and crossover points are widely used features among the blood vessel features. Non-blood vessel features include luminance, contrast, and corner points etc. This paper summarizes and compares the different retina based authentication system. Researchers have used publicly available databases such as DRIVE, STARE, VARIA, RIDB, ARIA, AFIO, DRIDB, and SiMES for testing their methods. Various quantitative measures such as accuracy, recognition rate, false rejection rate, false acceptance rate, and equal error rate are used to evaluate the performance of different algorithms. DRIVE database provides 100% recognition for most of the methods. Rest of the database the accuracy of recognition is more than 90%.

The presented study concerns development of a facial detection algorithm operating robustly in the thermal infrared spectrum. The paper presents a brief review of existing face detection algorithms, describes the experiment methodology and selected algorithms. For the comparative study of facial detection three methods presenting three different approaches were chosen, namely the Viola–Jones, YOLOv2 and Faster-RCNN. All these algorithms were investigated along with various configurations and parameters and evaluated using three publicly available thermal face datasets. The comparison of the original results of various experiments for the selected algorithms is presented.

This paper presents a study on the influence of psychophysical stimuli on facial thermal emissions. Two independent groups of stimuli are proposed to investigate facial changes resulting from human stress and physical exhaustion. One pertains to physical effort while the other is linked to stress invoked by solving a simple written test. Subjects’ face reactions were measured through collecting and analysing long-wavelength infrared images. A methodology for numerical processing of images is proposed. Results of numerical analysis with respect to different facial regions of interest are provided. An automatic deep learning based algorithm to classify specific thermal face patterns is proposed. The algorithm consists of detection of regions of interests as well as numerical analysis of thermal energy emissions of facial parts. The results of presented experiments allowed the authors to associate emission changes in specific facial regions with psychophysical stimulations of the person being examined. This work proves high usability of thermal imaging to capture changes of heat distribution of face as reactions for external stimuli.

Development of facial recognition or expression recognition algorithms requires input data to thoroughly test the performance of algorithms in various conditions. Researchers are developing various methods to face challenges like illumination, pose and expression changes, as well as facial disguises. In this paper, we propose and establish a dataset of thermal facial images, which contains a set of neutral images in various poses as well as a set of facial images with different posed expressions collected with a thermal infrared camera. Since the properties of face in the thermal domain strongly depend on time, in order to show the impact of aging, collection of the dataset has been repeated and a corresponding set of data is provided. The paper describes the measurement methodology and database structure. We present baseline results of processing using state-of-the-art facial descriptors combined with distance metrics for thermal face reidentification. Three selected local descriptors, a histogram of oriented gradients, local binary patterns and local derivative patterns are used for elementary assessment of the database. The dataset offers a wide range of capabilities – from thermal face recognition to thermal expression recognition.

Finger vein biometric systems become increasingly more popular because they offer higher security comparing to other authentication solutions with respect to positive persons experience. Those systems operate on near infrared light (NIR) in wavelength range from around 700 to 1000 nm, however dedicated research to determine impact of NIR lighting on biometric system effectiveness has not been conducted and presented in the literature ever before. In this paper the study of correlation between wavelengths in NIR spectra and effectiveness of person identification in a biometric system is presented. To achieve that goal, a new model of image acquisition system allowing change of light wavelengths has been created and NIR finger vein dataset containing 11 556 images was established. Furthermore, this model was used to perform experimental work and proof that some NIR wavelengths better suit for vein patterns acquisition, allowing to increase the recognition effectiveness of finger vein biometric systems.

The aim of this paper is to compare the efficiency of various outlier correction methods for ECG signal processing in biometric applications. The main idea is to correct anomalies in various segments of ECG waveform rather than skipping a corrupted ECG heartbeat in order to achieve better statistics. Experiments were performed using a self-collected Lviv Biometric Dataset. This database contains over 1400 records for 95 unique persons. The baseline identification accuracy without any correction is around 86%. After applying the outlier correction the results were improved up to 98% for autoencoder based algorithms and up to 97.1% for sliding Euclidean window. Adding outlier correction stage in the biometric identification process results in increased processing time (up to 20%), however, it is not critical in the most use-cases.

Annual and interannual phenomena and canopy behavior of prickly comfrey (Symphytum asperum Lep.) were studied in a 10-year experiment with 25 measurement sessions during the growing season. The results confirm the importance of long-term experiments in studying plant phenomena, biometrics and behavior. Prickly comfrey produced a green canopy each year and growth started very early in spring. Maximum plant height was less than 160 cm. Annual phenomena (growth initiation, seedling phase, flower phase, seed phase, senescent phase), interannual phenomena (initiation and youth, reproduction, new generation formation, plant death) and two population cycles (colonization and expansion) were measured. The duration of annual development up to canopy death can be expressed as x+2x+3x+2x, where x is initial growth. The genetic structure and activity of prickly comfrey promotes generative development of the species. Its age can be measured over a single and several vegetation generations. The ability to change the angle of vertical stem growth after 9 weeks can be considered a functional behavior of prickly comfrey and part of its life strategy. The differences between the organs in the upper and lower parts are very considerable and should be taken into account in morphological descriptions of this species. The upper and lower stems and leaves showed differential growth. Both stem and leaves were densely setose. Old leaves were 3.8 times longer, 4 times broader and 2.4 times thicker than young leaves. Hairs were on average 3 times longer on old than on young leaves. Flowers had contact with pollinators making relatively long visits to them.

Various methods are used to obtain a superior palmprint recognition system. After selecting a palmprint image filter, using Gabor orientation scale pairs is an option to support the refinement of the verification process. Many researchers use the [8×5] pair for the value of the Gabor orientation scale in the field of palmprint recognition. However, from the experiments conducted, other Gabor pairs have more impact on system improvement. The problem is to get the most suitable value pairs for palmprint applications, so in this study, a comparison of seven kinds of Gabor pairs is carried out. This Gabor pair being compared applies using original images, PCA dimension reduction, and the Euclidean method. From the research that has been done, the pair of Gabor orientation scale [8 × 7] or image expansion of 56 will have the most significant impact compared to other pairs. Suppose the result of this Gabor pair is [8×7] by using other improvement systems, namely the 3W filter instead of the original image, KPCA to replace the PCA, and the cosine method in the matching method. In that case, it will increase the verification value by 99.611%. The trial value obtained can be an alternative method of choice for improving palmprint recognition.